Genome-wide Investigation of Alcohol Response: A Meta-Analytic Review and Polygenic Associations with AUD

(7)项目总结/摘要 具体目标 目前的建议首先旨在通过以下方式扩展先前对酒精反应水平（LR）的遗传研究 通过以下荟萃分析进行迄今为止最大的LR全基因组关联研究（GWAS） 多个样本与现存的SRE（酒精影响的自我评定）和GWAS数据。第二个目标是 使用来自所述LR的GWAS的汇总数据，在独立样本中创建多基因风险评分 为了确定LR对酒精的潜在遗传影响是否以及在多大程度上作为一种遗传因素， 澳元风险因素。 方法 为实现上述目标，将根据标准质量控制（QC）处理数据集。 程序（例如，安德森等人，2010）允许基因型插补到一个共同的参考面板， 使用适用于个体研究设计的方法对每个数据集进行GWA分析。对于所有确定的 样品（见所附贡献信）Marc Schuckit博士（顾问）将提供关键支持， 关于使用SRE表型的决定。所有数据分析程序将在 与Ian Gizer博士（申办者）、Arpana Agrawal博士（顾问）和相应数据集的协调 参与者应确保所有数据均按照双方商定的程序进行分析。以下 个体研究水平的GWA分析，将执行标准化的GWAS荟萃分析QC程序 （Winkler等人，2014年），并将使用 METAL中的固定效应模型（Willer等人，2010年）。最后，SRE荟萃分析的结果将用于 计算独立目标样本中的多基因风险评分（PRS），以检查 AUD结局的LR至酒精PRS。澳大利亚双胞胎家庭（OZ-ALC GWAS）样本访问自 基因型和表型数据库（dbGaP）将用于实现第二个目标。 长期目标 当前提案的首要目标是利用GWA荟萃分析程序和多基因 建模方法，以综合多个GWA数据集的定量数据，以推进我们的 了解AUD病因的遗传结构，以及明确的生物学风险 AUD发展的机制（即，LR为酒精）。通过多种基因信息的结合 数据集，目前的研究是很好的定位，以检查遗传因素的基础上，一个明确的 AUD、LR的内表型与酒精有关。此外，多基因预测模型将有助于扩大我们的 了解LR对酒精的多基因结构，以及共享的遗传贡献 LR与酒精和AUD之间的关系。通过更精确地了解LR的遗传结构， 以及随后对AUD的易感性，我们可以在整合这种遗传基因方面取得实质性进展。 治疗方法的信息，目的是开发个性化的AUD干预措施。 培养目标 目前的提案将使申请人在管理、集成和数据方面获得宝贵的经验 在大规模的遗传信息数据集中调查酒精使用结果的分析程序， 以及应用最先进的多基因建模方法来检查 基因组中遗传变异的综合效应。这些培训经验的性质将提供 通过与多个研究小组的密切合作，为申请人提供身临其境的体验。因此，在本发明中， 成功完成这些培训目标将为申请人的职业目标奠定基础， 获得终身学术职位，调查大型，财团衍生，遗传 数据集以及如何最好地利用这些研究的结果来改善临床结果。

(7) Project Summary/Abstract Specific Aims The current proposal first aims to extend previous genetic studies of level of response (LR) to alcohol by conducting the largest genome-wide association study (GWAS) of LR to date through the meta-analysis of multiple samples with extant SRE (Self-Rating of the Effects of Alcohol) and GWAS data. A second aim is to use summary data from the described GWAS of LR to create polygenic risk scores in an independent sample in order to determine whether, and to what extent, the genetic influences underlying LR to alcohol serve as a risk factor for AUD. Method Towards the abovementioned aims, datasets will be processed according to standard quality control (QC) procedures (e.g., Anderson et al., 2010) allowing for genotype imputation to a common reference panel and GWA analysis of each dataset using methods appropriate for the individual study designs. For all identified samples (see attached contribution letters) Dr. Marc Schuckit (consultant) will provide critical support in decisions regarding the use of the SRE phenotype. All data-analytic procedures will be conducted in coordination with Dr. Ian Gizer (sponsor), Dr. Arpana Agrawal (consultant), and the respective dataset contributors to ensure that all data are analyzed according to mutually-agreed upon procedures. Following individual study-level GWA analyses, standardized GWAS meta-analysis QC procedures will be carried out (Winkler et al., 2014) and a meta-analysis combining results from all samples will be conducted utilizing a fixed-effects model in METAL (Willer et al., 2010). Lastly, results from the SRE meta-analysis will be utilized to compute polygenic risk scores (PRS) in an independent target sample to examine the predictive ability of the LR to alcohol PRS for AUD outcomes. The Australian Twin Families (OZ-ALC GWAS) sample accessed from the database of Genotypes and Phenotypes (dbGaP) will be used to accomplish this second aim. Long-Term Objectives The over-arching goal of the current proposal is to utilize GWA meta-analytic procedures and polygenic modeling approaches to synthesize quantitative data across multiple GWA datasets in order to advance our understanding of the genetic architecture of AUD etiology, as well as a well-established biological risk mechanism of AUD development (i.e., LR to alcohol). Through the combination of multiple genetically-informed datasets, the current study is well-positioned to examine the genetic factors underlying a well-defined endophenotype of AUD, LR to alcohol. Additionally, polygenic prediction models will serve to expand our knowledge of the polygenic architecture underlying LR to alcohol, as well as the shared genetic contributions between LR to alcohol and AUD. By gaining a more precise understanding of the genetic architecture of LR, and the subsequent susceptibility for AUD, we can make substantial progress towards integrating this genetic information in treatment approaches with the goal of developing personalized AUD intervention efforts. Training Aims The current proposal will allow the applicant to gain valuable experience in management, integration, and data analysis procedures for investigating alcohol use outcomes in large-scale, genetically-informed datasets, as well as knowledge in the application of state-of-the-art polygenic modeling approaches for examining the aggregate effect of genetic variation across the genome. The nature of these training experiences will provide an immersive experience for the applicant through close collaboration with multiple research groups. Thus, successful completion of these training aims will provide the foundation for the applicant’s career objective of obtaining a tenure-track academic position investigating AUD phenotypes in large, consortia-derived, genetic datasets and how best to leverage findings from such studies to improve clinical outcomes.